Code for Thought

Making Machine Learning Reproducible


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Reproducibility efforts are community efforts, as this episode's guest Grigori Fursin makes very clear. But you also need the tools. 
For some time, Grigori worked on the Collective Knowledge (CK) Framework to help researchers and machine learning practitioners get the best out of their solutions. 
In this episode we talk about the challenges you face when trying to evaluate machine learning applications and taking them to production. And how tools like CK Framework and others can help.

  • https://cknowledge.org - Collective Knowledge (CK) Framework web site 


  • https://mlcommons.org/en/ - ML Commons, a non-profit organisation & community for tools around machine learning applications: in particular ML Perf for performance testing


  • https://github.com/mlcommons/ck  - CK framework GitHub repository

Get in touch

Thank you for listening! Merci de votre écoute! Vielen Dank für´s Zuhören!

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This podcast is licensed under the Creative Commons Licence: https://creativecommons.org/licenses/by-sa/4.0/

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Code for ThoughtBy Peter Schmidt

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